stats 10

Question 1

Binary variables are usually coded as

Answer 1

0 or 1

Question 2

dummy variable trap

Answer 2

perfect multicollinearity that results from the inclusion of dummy variables representing each possible value of a categorical variable

Question 3

Perfect multicollinearity

Answer 3

when there is an exact linear relationship between any two or more of a regression model’s independent variables.

Question 4

The coefficient for the binary X variable indicates the difference in the Y variable between the respective category and the

Answer 4

reference category (the one omitted in the dummy coding)

Question 5

This coefficient provides insights into

Answer 5

how the response varies across different groups

Question 6

Reference category

Answer 6

in a regression model, the value of a categorical independent for which we do not include a dummy variable

Question 7

Categorical independent variables can be used in

Answer 7

interactions

Question 8

Interactive models

Answer 8

multiple regression models that contain at least one independent variable that researchers create by multiplying together two or more independent variables

Question 9

Use an interaction model in multiple regression if

Answer 9

you suspect that the effect of one independent variable on the dependent variable varies depending on the level of another independent variable

Question 10

A significant interaction effect between income and voter status indicates that the increase in donations with income is greater for voters than for nonvoters, suggesting that voter status —- the relationship between income and donations

Answer 10

moderates

Question 11

Moderation

Answer 11

the alteration of the relationship between two variables by a third variable, indicating that the effect of one variable on an outcome changes depending on the level or category of the modifying variable

Question 12

Interaction effects can be modeled between — of two categorical variables, two numeric variables, or one of each.

Answer 12

any combination

Question 13

Interaction Between a Categorical and a Numeric
Variable

Answer 13

The effect of a numeric variable on the dependent variable is modified by a categorical variable

Question 14

Interaction Between Two Categorical Variables

Answer 14

In this case, the interaction term assesses how the effect of one categorical variable on the dependent variable changes based on the levels of another categorical variable

Question 15

Interaction Between Two Numeric Variables

Answer 15

Here, the interaction term assesses how the relationship between one numeric variable and the dependent variable changes at different levels of another numeric variable

Question 16

When an exposure and an outcome independently
cause a third variable, that variable is termed a —

Answer 16

‘collider’.

Question 17

Inappropriately controlling for a collider variable, by study design or statistical analysis

Answer 17

results in collider bias

Question 18

Influential case

Answer 18

a case in a regression model
which has either a combination of large leverage and a large squared residual or a large DFBETA score

Question 19

An influential case can be influential if it has large

Answer 19

leverage.

Question 20

Leverage

Answer 20

in a regression model, the degree to which an individual case is unusual in terms of its value for a single independent variable, or its particular combination of values for two or more independent variables

Question 21

A case can be influential if it has a large

Answer 21

squared residual value.

Question 22

A large residual value indicates that the
observed data point deviates markedly from

Answer 22

the predicted outcome

Question 23

A case can be influential if it has both

Answer 23

large leverage and a large squared residual value

Question 24

DFBETA is a diagnostic measure used in regression analysis to

Answer 24

assess the influence of
individual data points on the estimated coefficients
of the model. It quantifies the change in each regression coefficient when a specific observation is removed from the dataset.

Question 25

DFBETA score

Answer 25

A statistical measure for the calculation of the influence of an individual case on the value of a single parameter estimate

Question 26

How to deal with influential cases in regression

Answer 26

1. Check for data collection or management problems. 2. Don’t do anything. 3. Delete the relevant observations. 4. Dummy out the influential cases

Question 27

Dummying out

Answer 27

adding a dummy (binary) variable to a regression model to measure and isolate the effect of an influential observation

Question 28

High multicollinearity

Answer 28

in a multivariate regression model, when two or more of the independent variables in the model are extremely highly correlated with one another, making it difficult to isolate the distinct effects of each variable

Question 29

Signs of potential multicollinearity

Answer 29

* two or more of your independent variables are theoretically associated, * two or more of your independent variables are known to correlate, * the standard errors for your Beta coefficients are large, or * the R2 is unexpectedly large

Question 30

Micronumerosity

Answer 30

a situation in statistical analysis where the number of observations or data points is very small relative to the number of variables being analyzed. * This condition can lead to several issues, including overfitting, unreliable estimates of model parameters, and difficulty in generalizing findings to a larger population. * When a dataset is micronumerous, there may not be enough data to adequately capture the relationships between variables

Question 31

If you detect multicollinearity and cannot get more data, then you need to calculate and report the

Answer 31

model variance inflation factor (VIF).

Question 32

Variance inflation factor (VIF)

Answer 32

a statistical measure to detect the contribution of each independent variable in a multiple regression model to overall multicollinearity

Question 33

To calculate VIF, estimate an

Answer 33

auxiliary regression model

Question 34

Auxiliary regression model:

Answer 34

a model in which one of the independent variables, Xj, becomes the dependent variable and all of the other independent variables remain independent variables